Personalizing Lexical Simplification

John Lee, Chak Yan Yeung


Abstract
A lexical simplification (LS) system aims to substitute complex words with simple words in a text, while preserving its meaning and grammaticality. Despite individual users’ differences in vocabulary knowledge, current systems do not consider these variations; rather, they are trained to find one optimal substitution or ranked list of substitutions for all users. We evaluate the performance of a state-of-the-art LS system on individual learners of English at different proficiency levels, and measure the benefits of using complex word identification (CWI) models to personalize the system. Experimental results show that even a simple personalized CWI model, based on graded vocabulary lists, can help the system avoid some unnecessary simplifications and produce more readable output.
Anthology ID:
C18-1019
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
224–232
Language:
URL:
https://aclanthology.org/C18-1019
DOI:
Bibkey:
Cite (ACL):
John Lee and Chak Yan Yeung. 2018. Personalizing Lexical Simplification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 224–232, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Personalizing Lexical Simplification (Lee & Yeung, COLING 2018)
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PDF:
https://aclanthology.org/C18-1019.pdf